Green Regional Path paper: First results

1 Introduction

• In this notebook, we present the first results of an analysis of green development paths of Nordic regions
• It is based on patent data from 1990-2015 (PATSTAT, Autumn 2021 Edition)
• Analysis is done on all Nordic NUTS 2 regions (fractionalized patent allocation by inventor location, DOCDB family level)
• Industries are captured by NACE2 codes of patents according to the OECD IPC-NACE2 concordance table.
• Green patents are identified using the Y02 tag in the CPC classification

1.1 Preprocessing

### general options
Sys.setenv(LANG = "en")
options("scipen" = 100, "digits" = 4) # override R's tendency to use scientific notation

### Clean workspace
rm(list=ls())
graphics.off()

### Load packages (maybe need to be installed first)
# Standard
library(tidyverse) # General DS toolkit
library(magrittr) # For advanced piping

# Databases
library(DBI) # GEneral R database interface
library(RPostgres) # PostgreSQL interface driver 
library(dbplyr) # for dplyr with databases

# networks
library(tidygraph)
library(ggraph)
library(ggrepel)

# GEoplot
library(giscoR)
library(sf)

## LOAD DATA

# Regular tables
data_appln <- read_rds('../temp/data_appln.rds')
data_pers_appln  <- read_rds('../temp/data_pers_appln.rds')
data_nace2 <- read_rds('../temp/data_nace2.rds')

#data_person <- read_rds('../temp/data_person.rds')
#data_docdb_fam_cpc <- read_rds('../temp/data_docdb_fam_cpc.rds')

# Regional specialization
region_RTA <- read_rds('../temp/region_RTA.rds') %>% 
  mutate(country = nuts %>% str_sub(1,2),
         nuts_period = paste(nuts, 'P', period)) 

region_tech <- read_rds('../temp/region_tech.rds') %>% 
  mutate(country = nuts %>% str_sub(1,2),
         nuts_period = paste(nuts, 'P', period)) 

# Technology space
g_tech <- read_rds('../temp/g_tech.rds')

# Lists
list_nace2 <- read_rds('../temp/list_nace2.rds')

# Applicants
region_applt_appln <- read_rds('../temp/tbl_region_applt_appln.rds') %>% select(-applt_seq_nr, -invt_seq_nr)
region_applt <- read_rds('../temp/tbl_region_applt.rds')

## Defining parameters
n_cutoff = 50
n_cutoff_green = 25

## SELECT FOCUS REGIONS
reg_in = '' # c('SE232', 'NO043', 'DK012')
n_regions = 5

# Restrict to top N regions
select_region <- region_tech %>%
  group_by(country, nuts) %>%
  summarise(n = sum(weight_frac, na.rm = TRUE),
            n_Y = sum(weight_frac * Y_tag, na.rm = TRUE)) %>%
  ungroup() %>%
  group_by(country) %>%
  arrange(nuts, desc(n_Y)) %>%
  mutate(index = 1:n()) %>%
  ungroup() %>%
  filter(index <= n_regions | nuts %in% reg_in) %>%
  distinct(nuts) %>%
  pull(nuts)

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

rm(reg_in)

# Createdataframe with technology relatedness edgelist
tech_rel <- g_tech %E>%
  mutate(from_nace = .N()$name[from],
         to_nace = .N()$name[to]) %>%
  as_tibble() %>%
  mutate(from = from_nace %>% as.character(),
         to = to_nace %>% as.character()) %>%
  arrange(from, to) %>%
  select(from, to, weight)

tech_rel %<>%
  # Add opposite direction
  bind_rows(tech_rel %>% 
              rename(from_new = to, to_new = from) %>% 
              rename(from = from_new, to = to_new) %>%
              relocate(from, to)) %>%
  # Add self loops
  bind_rows(tech_rel %>%
              distinct(from) %>%
              mutate(to = from,
                     weight = 1)) %>%
  distinct(from, to, .keep_all = TRUE)

# Summarize Regions
region_RTA_agg <- region_RTA %>%
  group_by(country, nuts, period, nuts_period, Y_tag) %>%
  summarise(n_spec = rta_bin %>% sum(na.rm = TRUE),
            n_spec_count = (n_tech_region * rta_bin) %>% sum(na.rm = TRUE),
            HHI = sum((n_tech_region/sum(n_tech_region) * 100)^2) ) %>%
  ungroup() 

`summarise()` has grouped output by 'country', 'nuts', 'period', 'nuts_period'. You can override using the `.groups` argument.

2 Patent application development

• In the following, a brief descriptive analysis of the development of green and non-green patent application in the Nordics
• In addition, a breackdown of green patents by top green patenting reagions

# Dataframe with regions and technology fields
tech_dev <- region_RTA %>%
  select(country, period, nuts, nuts_period, nace_group, Y_tag, n_tech_region, rta, rta_bin) %>%
  arrange(country, nuts, nace_group, Y_tag, period) %>%
  group_by(country, nuts, nace_group, Y_tag) %>%
  mutate(n_tech_region_lag = lag(n_tech_region, 1),
         n_tech_region_delta = n_tech_region - n_tech_region_lag,
         pct_tech_region_delta = (n_tech_region - n_tech_region_lag) / ( n_tech_region_lag + 1),
         rta_lag = lag(rta, 1),
         rta_delta = rta - rta_lag,
         rta_bin_lag = lag(rta_bin, 1),
         rta_bin_delta = rta_bin - rta_bin_lag) %>%
  ungroup() %>%
  arrange(country, nuts, nace_group, Y_tag, period) 

data_appln %>%
  count(appln_filing_year, Y_tag) %>%
  ggplot(aes(x = appln_filing_year, y = n, col = Y_tag)) + 
  geom_line(key_glyph = "timeseries") +
  labs(title = 'Patent applications: Development',
       subtitle = 'All Nordic contries, by Y tag',
       x = 'Year',
       y = 'Number applications',
       col = 'Green')

3 Applicants

• Applicants of patents filed by inventors in the Nordics
• That can be domestic or foreign applicants

data_pers_appln %>%
  filter(nuts %in% select_region) %>%
  count(appln_filing_year, nuts, Y_tag, wt = weight_frac) %>%
  ggplot(aes(x = appln_filing_year, y = n, col = nuts)) + 
  geom_line(key_glyph = "timeseries") +
  facet_wrap(vars(Y_tag), scales = 'free') +
  labs(title = 'Patent applications: Development',
       subtitle = 'All Nordic contries',
       x = 'Year',
       y = 'Number applications, by region and Y tag',
       col = 'Nuts3')

region_applt_appln %<>%
  group_by(appln_id) %>%
  mutate(n_frac = 1 / n()) %>%
  ungroup() %>%
  left_join(region_applt %>% select(person_id, han_id, han_name, person_ctry_code, nuts), by = 'person_id') %>%
  left_join(data_appln %>% select(appln_id, docdb_family_id, appln_filing_year, period, Y_tag), by = 'appln_id') %>%
  left_join(data_nace2 %>% select(appln_id, nace_group) %>% group_by(appln_id) %>% mutate(nace_share = 1 / n()) %>% ungroup() %>% nest(nace = c(nace_group, nace_share)), by = 'appln_id') %>%
  drop_na()

# List main applicants
applt_stats <- region_applt_appln %>%
  group_by(han_id, han_name, person_ctry_code, nuts) %>%
  summarise(
    n_pat = sum(n_frac),
    n_Y = sum(n_frac * Y_tag),
    first_pat = min(appln_filing_year)
    ) %>%
  mutate(share_Y = n_Y / n_pat,
         age = 2016 - first_pat,
         incumbant = age >= 10 & n_pat >= n_cutoff) %>%
  ungroup() %>%
  arrange(desc(n_pat))

`summarise()` has grouped output by 'han_id', 'han_name', 'person_ctry_code'. You can override using the `.groups` argument.

applt_stats %>% head(200)

applt_stats %>% arrange(desc(n_Y)) %>% head(100)

4 Technology space general

• We calculate the relatedness of industries by co-occurence pattern following Hidalgo & Hausmann (2007)
• Revealed technological advantage (RTA) Is sepperatedly calculated for Y-tag and non-Y-tag patents.

# Share of incumbants by technology
region_techn_incumb <- region_applt_appln %>% 
  left_join(applt_stats %>% select(han_id, incumbant), by = 'han_id') %>%
  unnest(nace) %>%
  mutate(n_weight = n_frac * nace_share) %>%
  group_by(nuts, nace_group, period, Y_tag) %>%
  summarise(n = sum(n_weight),
            n_inc = sum(n_weight * incumbant)) %>%
  ungroup() %>%
  mutate(share_inc = n_inc / n)

`summarise()` has grouped output by 'nuts', 'nace_group', 'period'. You can override using the `.groups` argument.

set.seed(1337)
coords_tech <- g_tech %>% igraph::layout.fruchterman.reingold() %>% as_tibble()
colnames(coords_tech) <- c("x", "y")

5 Regional specialization (RTA) development

• Comparison of specialization provides in period 1 and 2

g_tech %N>%
  mutate(nace_group_name = nace_group_name %>% str_trunc(50, side = 'right')) %>%
  ggraph(layout =  coords_tech) + 
  geom_edge_link(aes(width = weight, alpha = weight), colour = "grey") + 
  geom_node_point(aes(colour = nace_sec_name, size = dgr)) + 
  geom_node_text(aes(label = nace_group_name, size = dgr, filter = percent_rank(dgr) >= 0.75 ), repel = TRUE) +
  theme_void() +
  theme(legend.position="bottom") + 
  labs(title = 'Industry Space (all Nordics)',
       subtitle = 'Nodes = NACE 2 Industries. Edges: Relatedness')

p1 <- region_RTA_agg  %>%
  filter(nuts %in% select_region) %>%
  pivot_wider(names_from = Y_tag, values_from = c(n_spec, n_spec_count, HHI), values_fill = 0, names_prefix = 'Y_tag_') 

p2 <- p1 %>% 
  select(period, nuts, n_spec_Y_tag_FALSE, n_spec_Y_tag_TRUE) %>%
  pivot_wider(names_from = period, values_from = c(n_spec_Y_tag_FALSE, n_spec_Y_tag_TRUE))

6 Analysis for existing green paths:

p1 %>%
  ggplot(aes(x = n_spec_Y_tag_FALSE, y = n_spec_Y_tag_TRUE)) +
  geom_segment(data = p2, 
               aes(x = n_spec_Y_tag_FALSE_1,
                   y = n_spec_Y_tag_TRUE_1,
                   xend = n_spec_Y_tag_FALSE_2,
                   yend = n_spec_Y_tag_TRUE_2,
                   size = 0.75),
               alpha = 0.15,
               arrow = arrow(length = unit(0.5, "cm"), type = "closed"),
               show.legend = FALSE) +
  geom_point(aes(size = n_spec_count_Y_tag_TRUE, col = HHI_Y_tag_TRUE)) +
  geom_text_repel(aes(label = nuts), box.padding = 0.5, max.overlaps = Inf) +
  scale_color_gradient2(low = "skyblue", mid = 'yellow', high = "red", midpoint = 1) +
  scale_size(range = c(2, 10)) + 
  labs(title = 'Development of new regional specializations', 
       subtitle = 'By number of green and non green specializations in period 1 and 2',
       note = '',
       x = 'N non-green specializations',
       y = 'N green specializations',
       size = 'N green patents',
       col = 'HHI green patents') 

rm(p1, p2)

tech_rel_dev <- tech_rel %>% 
  select(from, to, weight) %>%
  left_join(tech_dev %>% distinct(nace_group, nuts), by = c('from' = 'nace_group')) %>%
  # filter for rta in period 1
  inner_join(tech_dev %>% filter(period == '1', rta_bin == 1) %>% select(nace_group, nuts, Y_tag), by = c('to' = 'nace_group', 'nuts')) %>%
  # filter for new green specialization in period 2
  semi_join(tech_dev %>% filter(period == '2', rta_bin == 1, rta_bin_delta == 1, Y_tag == TRUE), by = c('from' = 'nace_group', 'nuts')) %>%
  rename(nace_group = from, related_techn = to) 

p1 <- tech_rel_dev %>%
  group_by(nuts, nace_group, Y_tag) %>%
  summarise(rel_max = weight %>% max(),
            rel_sum = weight %>% sum(),
            rel_mean = weight %>% mean()) %>%
  ungroup() %>%
  #
  group_by(nuts, Y_tag) %>%
  summarise(rel = rel_max %>% mean()) %>%
  ungroup() %>%
  #
  pivot_wider(names_from = Y_tag, values_from = rel, names_prefix = 'Y_', values_fill = 0) %>%
  left_join(tech_dev %>% filter(Y_tag == TRUE, period == '2', rta_bin == 1) %>% select(nuts , n_tech_region) %>% count(nuts, wt = n_tech_region), by = c('nuts')) %>%
  mutate(country = nuts %>% str_sub(1,2)) 

`summarise()` has grouped output by 'nuts', 'nace_group'. You can override using the `.groups` argument.`summarise()` has grouped output by 'nuts'. You can override using the `.groups` argument.

x_mid <- mean(p1$Y_FALSE, na.rm = TRUE)
y_mid <- mean(p1$Y_TRUE, na.rm = TRUE)

p1 %>%
  filter(0.5 <= percent_rank(n)) %>%
  ggplot(aes(x = Y_FALSE, y = Y_TRUE, size = n)) +
  geom_vline(xintercept = x_mid, linetype = "dashed", color = 'grey') + 
  geom_hline(yintercept = y_mid, linetype = "dashed", color = 'grey') +
  geom_point(aes(col = country)) +
  geom_text_repel(aes(label = nuts), box.padding = 0.5, max.overlaps = Inf) +
  theme(legend.position="bottom") + 
    labs(title = 'New green specialization period 2', 
       subtitle = 'By nuts regions',
       note = 'Relatedness is the mean over all new green specializations, per green specialization largest relatedness to former specialization counted',
       x = 'Relatedness non-green',
       y = 'Relatedness green',
       size = 'N green patents') 

rm(p1, x_mid, y_mid)

7 Profiling regions

p1 <- tech_rel_dev %>%
  filter(nuts %in% select_region) %>%
  group_by(nuts, nace_group, Y_tag) %>%
  summarise(rel = weight %>% max()) %>%
  ungroup() %>%
  pivot_wider(names_from = Y_tag, values_from = rel, names_prefix = 'Y_', values_fill = 0) %>%
  left_join(tech_dev %>% 
              filter(Y_tag == TRUE, period == '2', rta_bin_delta == 1) %>% 
              select(nuts, nace_group, n_tech_region) %>% 
              count(nuts, nace_group, wt = n_tech_region), 
            by = c('nuts', 'nace_group')) %>%
  mutate(country = nuts %>% str_sub(1,2)) %>%
  left_join(list_nace2 %>%select(nace_group, nace_sec_name), by = 'nace_group')

`summarise()` has grouped output by 'nuts', 'nace_group'. You can override using the `.groups` argument.

#x_mid <- mean(c(max(p1$Y_FALSE, na.rm = TRUE), 
#                min(p1$Y_FALSE, na.rm = TRUE)))

#y_mid <- mean(c(max(p1$Y_TRUE, na.rm = TRUE), 
#                min(p1$Y_TRUE, na.rm = TRUE)))

x_mid <- mean(p1$Y_FALSE, na.rm = TRUE)
y_mid <- mean(p1$Y_TRUE, na.rm = TRUE)

# plotting
p1 %>%
  ggplot(aes(x = Y_FALSE, y = Y_TRUE, size = n, col = nace_sec_name)) +
  geom_point() +
  geom_text_repel(aes(label = nace_group), box.padding = 0.5) +
  geom_vline(xintercept = x_mid, linetype = "dashed", color = 'grey') + 
  geom_hline(yintercept = y_mid, linetype = "dashed", color = 'grey') +
  facet_wrap(vars(nuts)) +
  labs(title = 'New green specialization period 2', 
       subtitle = 'By nuts regions',
       note = 'Relatedness is the mean over all new green specializations, per green specialization largest relatedness to former specialization counted',
       x = 'Relatedness non-green',
       y = 'Relatedness green',
       size = 'N green patents') 

rm(p1, x_mid, y_mid)

path_green_new <- tech_rel_dev %>%
  group_by(nuts, nace_group, Y_tag) %>%
  summarise(rel = weight %>% max()) %>%
  ungroup() %>%
  pivot_wider(names_from = Y_tag, values_from = rel, names_prefix = 'Y_', values_fill = 0) %>%
  left_join(tech_dev %>% 
              filter(Y_tag == TRUE, period == '2', rta_bin_delta == 1) %>% 
              select(nuts, nace_group, n_tech_region) %>% 
              count(nuts, nace_group, wt = n_tech_region), 
            by = c('nuts', 'nace_group')) %>%
  mutate(green_path = case_when( 
    Y_FALSE <= mean(Y_FALSE) & Y_TRUE <= mean(Y_TRUE) ~ 'creation',
    Y_FALSE <= mean(Y_FALSE) & Y_TRUE > mean(Y_TRUE) ~ 'diversification',
    Y_FALSE > mean(Y_FALSE) & Y_TRUE <= mean(Y_TRUE) ~ 'renewal',
    Y_FALSE > mean(Y_FALSE) & Y_TRUE > mean(Y_TRUE) ~ 'renew+div'
    ) ) %>%
  select(-Y_FALSE, - Y_TRUE)

`summarise()` has grouped output by 'nuts', 'nace_group'. You can override using the `.groups` argument.

• Below a radar plot summing all up.
• It includes the share of green patents in nace groups folllowing a particular green path (within a nuts region)
• Color indicates the share of incumbents (+10 years, +50 patents) in the path.
• Can be used to identify a regions main path and overal profile.

path_green <- tech_dev %>% 
  mutate(green_path = case_when( 
    Y_tag == TRUE & period == '2' & rta_bin == 1 & rta_bin_delta == 0 & rta_delta >= 0.1 ~ 'extension',
    Y_tag == TRUE & period == '2' & rta_bin == 0 & rta_bin_delta == -1 ~ 'extinction'
  )) %>%
  drop_na(green_path) %>%
  select(nuts, nace_group, n_tech_region_delta, green_path) %>%
  rename(n = n_tech_region_delta) %>%
  # add existing green paths
  bind_rows(path_green_new) %>%
  mutate(n = n %>% abs()) %>%
  # add incumbant measures
  left_join(region_techn_incumb %>% filter(period == '2', Y_tag == TRUE) %>% select(nuts, nace_group, share_inc), by = c('nuts', 'nace_group')) %>%
  mutate(n_new = n * (1 - share_inc),
         n_inc = n * share_inc)

8 Geoplotting

• I also added a first plotting of main green paths
• Works well, so we can adsd furthr geoplots if necessary.

path_green %>%
  filter(nuts %in% select_region) %>%
  # split by inc and non_incumbents
  pivot_longer(c(n_new, n_inc), names_to = 'applt_type') %>% 
  # Aggregate
  count(nuts, green_path, applt_type, wt = value) %>%
  # Add overall patents andf make share
  left_join(region_RTA %>%filter(period == '2') %>%  count(nuts, wt = n_region, name = 'n_reg'), by = 'nuts') %>%
  mutate(n_share = n / n_reg) %>%
  # plotting
  ggplot() +
  geom_col(aes(x = green_path, y = n_share, fill = applt_type, col = green_path), alpha = 0.8, position= "stack")  + 
  # Lollipop shaft 
  geom_segment( aes(x = green_path, y = 0, xend = green_path, yend = 0.002), linetype = "dashed", color = "gray12") + 
  coord_polar() +
  facet_wrap(vars(nuts), ncol = n_regions) +
  theme(legend.position = 'bottom') +
  labs(title = 'Radar PLot: Regional green paths', 
       subtitle = 'By nuts regions',
       note = 'xxxx',
       x = NULL,
       y = NULL,
       size = 'Share green patents',
       col = 'Green path type',
       fill = 'Applicant type') 

# See: https://ropengov.github.io/giscoR/ 

# Get map of nordics
map_nordic <- gisco_get_nuts(country = c('DNK', 'SWE', 'NOR', 'FIN'), nuts_level = 3, year = '2021')

# filter out Svalbart etc
map_nordic %<>%
  filter(!(NUTS_ID %in% c('NO0B1', 'NO0B2')))

# Group by NUTS by country and convert to lines
country_lines <- map_nordic %>%
  group_by(CNTR_CODE) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  st_cast("MULTILINESTRING")

9 Tech Space changes

map_nordic %>%
  # enter main green path
  left_join(path_green %>% count(nuts, green_path, wt = n) %>% group_by(nuts) %>% slice_max(order_by = n, n = 1, with_ties = FALSE) %>% ungroup(), by = c('NUTS_ID' = 'nuts')) %>%
  # plot
  ggplot() + 
  geom_sf(aes(fill = green_path)) +
  geom_sf(data = country_lines, col = "blue", linewidth = 0.1) + 
  theme_void() +
  labs(title = 'Map: Nordic main green paths', 
       subtitle = 'By nuts regions',
       note = 'xxxx',
       x = NULL,
       y = NULL,
       fill = 'Main green path') 

plot_techspace_dev <- function(g, rta_df, dev_df, region, time = '2', layout_nw = 'nicely'){
  # TODO, C&P function from below once finished
}

TODO: GO ON HERE AND DO BETTER DATAVIZ

# plot_techspace_dev(g = g_tech, rta_df = tech_dev, region = 'DK013', layout_nw = coords_tech) 

10 Endnotes

# TEst for function development
g = g_tech
rta_df = tech_dev
dev_df = tech_rel_dev 
region = 'DK013'
time = '2'
layout_nw = coords_tech

rta_df %<>% 
  filter(nuts == region, period == time, Y_tag == TRUE) %>% 
  select(nace_group, rta, n_tech_region)
  
dev_df %<>%
  filter(nuts == region) %>% 
  group_by(nace_group) %>%
  summarise(prev_nongreen = max(nace_group == related_techn, na.rm = TRUE) %>% as.logical()) %>%
  ungroup() %>%
  replace_na(list(prev_nongreen = FALSE)) %>%
  select(nace_group, prev_nongreen)

g <- g %N>%
  mutate(label = nace_group_name %>% str_trunc(50, side = 'right')) %>%
  left_join(rta_df, by = c("name" = "nace_group")) %N>%
  left_join(dev_df, by = c("name" = "nace_group")) 

g %>%
  ggraph(layout =  coords_tech) + 
  geom_edge_link(aes(width = weight, alpha = weight), colour = "grey") + 
  geom_node_point(aes(colour = rta, shape = prev_nongreen, size = n_tech_region, filter = rta >= 1)) + 
  geom_node_text(aes(label = label, size = n_tech_region, filter = rta >= 1), repel = TRUE) +
  scale_color_gradient2(low = "skyblue", mid = 'yellow', high = "red", midpoint = 1) +
  theme_void() +
  theme(legend.position="bottom") + 
  labs(title = paste("Industry Space:", region, sep = " "),
       subtitle = 'Nodes = NACE 2 Industries. Edges: Relatedness',
       caption = '')

select_regions_green <- tech_dev %>%
  group_by(nuts, period) %>%
  summarise(green =  sum(Y_tag * rta, na.rm = TRUE),
            green_bin =  sum(Y_tag * rta_bin, na.rm = TRUE),
            n_tech_region =  sum(n_tech_region, na.rm = TRUE),
            n_green_region =  sum(Y_tag * n_tech_region, na.rm = TRUE),
            n_green_rta =  sum(Y_tag * n_tech_region * rta_bin, na.rm = TRUE)) %>%
  ungroup() %>%
  filter(n_green_rta >= n_cutoff,
         green_bin >= 1,
         period == '1') %>%
  select(nuts)

tech_spec_dev <- tech_dev %>%
  filter(n_tech_region >= n_cutoff_green) %>%
  group_by(nuts, Y_tag) %>%
  summarise(rta_delta = rta_delta %>% sum(na.rm = TRUE)) %>%
  ungroup() %>%
  pivot_wider(names_from = Y_tag, values_from = rta_delta, values_fill = 0, names_prefix = 'Y_spec_')

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